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共 585 条记录，以下是321-330 订阅

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data mining Application in Smart Meter Quality Control

Data Mining Application in Smart Meter Quality Control

引用

international Conference on Fuzzy System and data mining (FSDM)

作者： Zhu, Yu-Nan Ji, Feng Liu, Jian Tian, Zheng-Qi Zhou, Chao Mahjoubfar, Ata Elect Power Metering Key Lab State Grid 9 Aoti St Nanjing Jiangsu Peoples R China State Grid Jiangsu Elect Power Res Inst 9 Aoti St Nanjing Jiangsu Peoples R China Univ Calif Los Angeles Dept Elect Engn Los Angeles CA 90024 USA Calif NanoSyst Inst Los Angeles CA USA

ISBN: (纸本)9781614996194;9781614996187

This paper presents application of data mining in enhancement of smart meter quality control based on the observed life cycle data. data mining techniques followed by machine learning methods efficaciously recognize intricate smart meter usage and failure patterns, generating unique insights, which lead to solutions for better quality control results.

关键词： data mining quality control smart meter pattern recognition

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CEUR workshop Proceedings

CEUR Workshop Proceedings

引用

5th Joint workshop on data mining and Knowledge Discovery meets Linked Open data and the 1st international workshop on Completing and Debugging the Semantic Web, Know@LOD 2016 and CoDeS 2016

The proceedings contain 9 papers. The topics discussed include: extending FrameNet to machine learning domain;PageRank on Wikipedia: towards general importance scores for entities;learning semantic rules for intelligent transport scheduling in hospitals;the linked data mining challenge 2016;not-so-linked solution to the linked data mining challenge 2016;a hybrid method for rating prediction using linked data features and text reviews;can you judge a music album by its cover?;finding and avoiding bugs in enterprise ontologies;and a two-fold quality assurance approach for dynamic knowledge bases: the 3cixty use case.

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Deep Convolutional Neural Networks-Based Age and Gender Classification with Facial Images 1

Deep Convolutional Neural Networks-Based Age and Gender Clas...

引用

1st international Conference on Electronics Instrumentation & Information Systems (EIIS)

作者： Liu, Xuan Li, Junbao Hu, Cong Pan, Jeng-Shyang Harbin Inst Technol Dept Automat Test & Control Harbin 150080 Heilongjiang Peoples R China Guilin Univ Elect Technol Sch Elect Engn & Automat Guilin 541004 Peoples R China Fujian Univ Technol Fujian Prov Key Lab Big Data Min & Applicat Fuzhou 350108 Fujian Peoples R China

ISBN: (纸本)9781538608432

in this paper, we build an age and gender classification system including two networks to classify age and gender based on GoogLeNet with the help of Caffe deep learning framework. It outputs gender and age groups of the facial images captured from the camera. We use Adience dataset to train GoogLeNet. Asynchronous stochastic Gradient Descent based on multi-GPUs is used to optimize training process. We intend to use the trained network to build a classification system in real world to show the practicability. For instance, it can apply to a targeted delivery in bus stop or department store. The results indicate that the accuracy of the classification network can be improved by pre-training. In addition, the multi-GPUs training platform can improve the training speed during the recognition. Overall system reaches speed of 8fps with a high accuracy to classify age and gender.

关键词： age and gender classification Deep Conventional Neural Networks real-time recognition system

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On the usage of active learning for SHM 27

On the usage of active learning for SHM

引用

international Conference on Noise and Vibration Engineering (ISMA) / international Conference on Uncertainty in structural Dynamics (USD)

作者： Dervilis, N. Papatheou, E. Antoniadou, I. Cross, E. J. Worden, K. Univ Sheffield Dept Mech Engn Dynam Res Grp Mappin St Sheffield S1 3JD S Yorkshire England

ISBN: (纸本)9789073802940

The key element of this work is to demonstrate a strategy for using pattern recognition algorithms to investigate correlations between feature variables for structural Health Monitoring (SHM). The task will take advantage of data from a bridge. An informative chain of artificial intelligence tools will allow an active learning interaction between the unfolded shapes of the manifold of online data by characterising the physical shape between variables. In many data mining and machine learning applications, there is a significant supply of unlabelled data but an important undersupply of labelled data. Semi-supervised active learning, which combines both labelled and unlabelled data can offer serious access to useful information and may be the crucial element in successful decision making, regarding the health of structures.

关键词： structural health monitoring

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Text mining in Clinical Domain: Dealing with Noise 16

Text Mining in Clinical Domain: Dealing with Noise

引用

22nd ACM SIGKDD international Conference on Knowledge Discovery and data mining (KDD)

作者： Hoang Nguyen Patrick, Jon CSIRO Data61 13 Garden St Eveleigh NSW 2015 Australia Univ Sydney 1 Cleveland St Sydney NSW 2006 Australia

ISBN: (纸本)9781450342322

Text mining in clinical domain is usually more difficult than general domains (e.g. newswire reports and scientific literature) because of the high level of noise in both the corpus and training data for machine learning (ML). A large number of unknown word, non-word and poor grammatical sentences made up the noise in the clinical corpus. Unknown words are usually complex medical vocabularies, misspellings, acronyms and abbreviations where unknown non words are generally the clinical patterns including scores and measures. This noise produces obstacles in the initial lexical processing step as well as subsequent semantic analysis. Furthermore, the labelled data used to build ML models is very costly to obtain because it requires intensive clinical knowledge from the annotators. And even created by experts, the training examples usually contain errors and inconsistencies due to the variations in human annotators' attentiveness. Clinical domain also suffers from the nature of the imbalanced data distribution problem. These kinds of noise are very popular and potentially affect the overall information extraction performance but they were not carefully investigated in most presented health informatics systems. This paper introduces a general clinical data mining architecture which is potential of addressing all of these challenges using: automatic proof-reading process, trainable finite state pattern recogniser, iterative model development and active learning. The reportability classifier based on this architecture achieved 98.25% sensitivity and 96.14% specificity on an Australian cancer registry's held-out test set and up to 92% of training data provided for supervised ML was saved by active learning.

关键词： Clinical active learning text classification named-entity recognition natural languages processing

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Distributed pattern recognition within DHT-Based Networks for Imbalanced datasets 1

Distributed Pattern Recognition within DHT-Based Networks fo...

引用

1st IEEE international Conference on Computer Communication and the Internet (ICCCI)

作者： Amir, Amiza Srinivasan, Bala Khan, Asad I. Univ Malaysia Perlis Sch Comp & Commun Engn Perlis Malaysia Monash Univ Fac Informat Technol Melbourne Vic Australia

ISBN: (纸本)9781467385152

This paper studies the accuracy of a fully-distributed pattern recognition algorithm, namely the P2P-GN, with imbalanced datasets problem which is often neglected by most of the available distributed algorithms. A major distinction of the P2P-GN compared to the other approaches is that it forms a single global classifier, instead of building many local classifiers ( one at every site). Fine-granularity components of the classifier are distributed across the network by using Distributed Hash Table (DHT) - which provides efficient linking to these components and ensures the system remains fully-distributed. Our experimental results also show that the P2P-GN can produce highly accurate results despite imbalanced data distribution compared to other distributed algorithms (Ivote-DPV, ensemble ID3, ensemble k-NN, and ensemble naive Bayes).

关键词： P2P-based classification distributed machine learning efficient distributed algorithm distributed classification

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Early Experiences with Crowdsourcing Airway Annotations in Chest CT 1st

引用

2nd international workshop on Deep learning in Medical Image Analysis (DLMIA) / 1st international workshop on Large-Scale Annotation of Biomedical data and Expert Label Synthesis (LABELS)

作者： Cheplygina, Veronika Perez-Rovira, Adria Kuo, Wieying Tiddens, Harm A. W. M. de Bruijne, Marleen Erasmus MC Biomed Imaging Grp Rotterdam Dept Med Informat Rotterdam Netherlands Erasmus MC Dept Radiol Rotterdam Netherlands Delft Univ Technol Pattern Recognit Lab Delft Netherlands Sophia Childrens Univ Hosp Erasmus Med Ctr Dept Pediat Pulmonol & Allergol Rotterdam Netherlands Erasmus MC Dept Radiol Rotterdam Netherlands Univ Copenhagen Dept Comp Sci Image Sect Copenhagen Denmark

ISBN: (纸本)9783319469768;9783319469751

Measuring airways in chest computed tomography (CT) images is important for characterizing diseases such as cystic fibrosis, yet very time-consuming to perform manually. machine learning algorithms offer an alternative, but need large sets of annotated data to perform well. We investigate whether crowdsourcing can be used to gather airway annotations which can serve directly for measuring the airways, or as training data for the algorithms. We generate image slices at known locations of airways and request untrained crowd workers to outline the airway lumen and airway wall. Our results show that the workers are able to interpret the images, but that the instructions are too complex, leading to many unusable annotations. After excluding unusable annotations, quantitative results show medium to high correlations with expert measurements of the airways. Based on this positive experience, we describe a number of further research directions and provide insight into the challenges of crowdsourcing in medical images from the perspective of first-time users.

关键词： learning systems

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Experimental evaluation on machine learning techniques for human activities recognition in digital education context 1

引用

1st international workshop on Social Computing in Digital Education, SOCIALEDU 2015

作者： Leitão, Gabriel Colonna, Juan Ribeiro, Erick Barreto, Raimundo Araujo, Thierry-Yves Martins, Anny Koster, Andrew Koch, Fernando Manaus Brazil Samsung Research Institute CampinasSP France

ISBN: (数字)9783319396729

ISBN: (纸本)9783319396712

This paper describes an experimental evaluation of the main machine learning supervised techniques to be used for the human activities recognition in the context of technological education using data collected from smartphones sensors. The overall goal is to use the recognition of activities to identify students with attention deficit or hyperactivity problems, by recognizing three activities: walking, standing and sitting. Hence, this work focuses on developing activities recognition method of the students. The methodology consists in: collecting data where the user explicitly states what activity he/she is doing;applying various techniques to automatically recognize the activities;and measuring the degree of accuracy of each technique. The results shows that techniques such as Bayesian inference and SVM (Support Vector machine) have smaller accuracy than techniques based on decision tree and kNN (k-nearest neighbors). Furthermore, the techniques based on decision trees have a constant computational cost, while the kNN depends on the number of samples. © Springer international Publishing Switzerland 2016.

关键词： students

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machine learning Techniques for Intrusion Detection: A Comparative Analysis 16

Machine Learning Techniques for Intrusion Detection: A Compa...

引用

1st international Conference on Informatics and Analytics (ICIA)

作者： Hamid, Yasir Sugumaran, M. Journaux, Ludovic Pondicherry Engn Coll Dept Comp Sci & Engn Pondicherry India Univ Burgundy Lab Le2i Dijon France

ISBN: (纸本)9781450347563

With the growth of internet world has transformed into a global market with all monetary and business exercises being carried online. Being the most imperative resource of the developing scene, it is the vulnerable object and hence needs to be secured from the users with dangerous personality set. Since the Internet does not have focal surveillance component, assailants once in a while, utilizing varied and advancing hacking topologies discover a path to bypass framework's security and one such collection of assaults is Intrusion. An intrusion is a movement of breaking into the framework by compromising the security arrangements of the framework set up. The technique of looking at the system information for the conceivable intrusions is known intrusion detection. For the last two decades, automatic intrusion detection system has been an important exploration point. Till now researchers have developed Intrusion Detection Systems (IDS) with the capability of detecting attacks in several available environments;latest on the scene are machine learning approaches. machine learning techniques are the set of evolving algorithms that learn with experience, have improved performance in the situations they have already encountered and also enjoy a broad range of applications in speech recognition, pattern detection, outlier analysis etc. There are a number of machine learning techniques developed for different applications and there is no universal technique that can work equally well on all datasets. In this work, we evaluate all the machine learning algorithms provided by Weka against the standard data set for intrusion detection i.e. KddCupp99. Different measurements contemplated are False Positive Rate, precision, ROC, True Positive Rate.

关键词： False Positive IDS machine learning Precision ROC True Positive

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The study of the Compatibility Rules of Traditional Chinese Medicine Based on Apriori and HMETIS Hypergraph Partitioning Algorithm 1st

The Study of the Compatibility Rules of Traditional Chinese ...

引用

1st international workshop on data Management and Analytics for Medicine and Healthcare (DMAH) / workshop on Big-Graphs Online Querying (Big-O (Q))

作者： Wang, Miao Li, Jiayun Chen, Li Huang, Yanjun Zhou, Qiang Che, Lijuan Shang, Huiliang Shanghai Univ TCM 1200 Cailun Rd Shanghai 201210 Peoples R China Fudan Univ Dept Elect Engn 220 Handan Rd Shanghai 200433 Peoples R China

ISBN: (纸本)9783319415765;9783319415758

One of the major research contents carried by scholars of Traditional Chinese medical science (TCM) is to discover the compatibility rules of herbs to increase the efficacy in treating certain syndromes. However, up to now, most of the compatibility rules of herbs are based on empirical analyses, which make them hard to study. Since concepts of Big data and machine learning have been popularized gradually, how to use data mining techniques to effectively figure out core herbs and compatibility rules becomes the main research aspect of TCM informatics. In this paper, the hypergraph partitioning algorithm HMETIS based on Apriori is applied to exploit and analyze clinical data about lung cancer. The result shows that all 15 Chinese herbs obtained by the algorithm accord with the core concepts of the treatment of lung cancer by experienced TCM doctors, namely replenishing nutrition, clearing heat-toxin, resolving phlegm and eliminating pathogenic factors.

关键词： data mining Compatibility rules of herbs Apriori algorithm Hypergraph Community partitioning HMETIS

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